aabajian

I'm going to offer a use case for touch screens: Augmented reality (AR) development.

I think there's a real possibility that AR takes off in the next 1-5 years. The combination of LLMs + smaller AR glasses makes AR a viable computing platform.

For example, simple things like, "Where are my keys?" is a lot easier to solve if (a) you're wearing your AR glasses already and (b) they have access to a frontier LLM.

However, it's very difficult to simulate "touching something" in AR using a mouse/trackpad and keyboard. What if you want to "twist open" a peanut butter jar? I could imagine touching the screen and twisting my fingers in a rotational pattern along the top of the can.

Can anyone answer me: What is the hardware cost to run a single instance of a frontier model? I've heard everything from $25K to $3M.

I think it comes down to: Will the hardware cost for frontier models come down?

Also, here's a business idea: Web crawler for open weight models. You just pay a monthly fee so that your home model can continuously update on a large web crawl each hour/day.

I don't think this is avoidable even at the present day. Anyone with sufficient capital could download a leading open-source model and run it on ~$10-25K hardware. They could align it to their own interest, including making a biological weapon. This law is akin to blocking Torrent downloads. Sure, you can slow them, but there will always be back channels.

If I'm understanding this correctly, it uses the electric motor to compress air into large tanks? How much thrust could that generate (and for how long)? It'd be amazing to jump over East River or the Hudson.

I recently had a delivery to my Kaiser office in Portland. It was printer ink! My printer had run out of ink. Except, I don't own a printer, and I've never owned one that self-reorders ink. Someone at some time had registered their printer to my office address and enabled self-reordering of printer ink.

It's a tool to help with catheter choice, C-arm angle, and contrast injection rate. These are things I think about in every case anyhow, might as well add some data to drive my decisions. If the results don't match reality, I can always fall-back on my personal defaults.

I think the takeaway is that we have to think bigger, much bigger, now that AI coding is essentially "solved". Anyone can "write" an ARM simulator now, so that can't be the pinnacle of engineering. I'm thinking about buildings that couldn't be built (reasonably) by one person.

I'll give an example of what I'm working on. There's a model for segmenting organs from CT scans. There's another model for calculating radial diameters of blood vessels from CT scans. You can combine the two and approximate how much blood flow each organ gets based on its segmented volume and the sum total radial diameter of vessels feeding it. You can then render all of that in an app that shows perfusion to that organ.

The idea is to combine these models to extract every ounce of usable information from a CT scan that might be useful for procedural planning (I'm an interventional radiologist). While each algorithm would take weeks/months to implement, Opus 5.5 can do them in minutes to hours and then stitch them together. The results thus far have been better than anything I've seen.

Opus 5.5 is on another level, especially when it comes to mathematics implementations. You can drop it a PhD-level physical simulation (for example, a contrast-injection simulation for angiography in my case), and it just...implements it. With full-on WebGL rendering in the browser, from scratch (or using an existing library, if you prefer).

I don't mean to be mean, but the problem is that AI has exposed how much programming is designed around human usability. C/Rust/Go wrap assembly. TypeScript wraps JS. Java/Python wrap C.

Do you need all these layers of abstraction when the human is no longer looking at the code?

Best analogy is forgetting how to use a slide rule following the advent of calculators. The former was made to make hand-calculation of logarithms easy. The latter does these calculations directly (obviating the need for a slide rule at all).

I think what humans still need to learn are the theory and domain fundamentals for their industry. If that industry is computer science, that means algorithms, calculus, linear algebra, etc. I think the future of CS is then (a) theoretical human-drive design and (b) prompt engineering to implement and verify that design.

It would also be helpful to have domain knowledge outside of CS as having the skills to build something is nearly commoditized (outside of the above fundamentals).

This article...does not explain how to run frontier AI on your own hardware. If that were possible, it would drop the floor out from under OpenAI and Anthropic.

The answer to this is: countries with the most natural resources will build robots to farm all their food, mine all their minerals, and build all their products. It will be up to governments to enforce that outputs are equitably distributed to the populace. Countries without natural resources, or ones with corrupt governments, will continue to have serious, and likely worsening, problems.

Thought experiment: If no thought workers are needed to design or engineer a Ferrari, what is needed? My answer is time and natural resources (include energy).

Law is so ripe for replacement by AI. It is entirely based on established written arguments with gray areas, edge cases, linguistic subtleties, etc., being the crux upon which many controversial decisions are drawn. That is, law arguments are based on a mastery of language coupled with knowledge of legal precedent. LLMs are extremely good at linguistic reasoning and can be trained on ever legal case every published.

I shipped my iOS app without opening Xcode (well, I had to open it to get some provisioning stuff to work). I still use Visual Studio Code to kick off the simulator, but other than that, I use no IDE at all. Claude writes all the code in Flutter.

IDEs are dead, prove me wrong.

I don't think anyone has mentioned the obvious middle ground: PO Boxes at USPS post offices. Nearly every town and city in the USA has a post office. Instead of driving packages to individual's homes or having businesses deliver to their specific warehouses, the middle ground is to deliver everything to the USPS offices.